Risk Analysis in money laundering
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Money laundering is among the leading concerns affecting banks and other financial institutions across the globe. This research was performed to provide a comprehensive understanding of the risk of money laundering experienced by banks to underlie the formulation of models for identification, analysis, and management of this risk. The research involved two models, risk assessment and customer behavior models. The former model comprised money laundering risk, as the dependent variable, and various independent variables, including private entity’s nationality, private entity’s country of residence, private entity’s profession, corporate entity’s headquarters, corporate entity’s country of residence, and corporate entity’s economic activity. Conversely, the behavioral model, where customer behavior suspicion formed the explained variable, the counter number used by a client, client number, transaction operation code, and currency of the transaction, were the explanatory variables. AA Bank in Europe served as the sample for this study; 107 corporate entities, 107 private entities, and 100 individual clients were involved. Data for 2019 was obtained and regressively analyzed using the Excel Program. The study outcomes indicated that a direct relationship exists between money laundering risk and private entity’s country of residence, private entity’s profession, private entity’s nationality, corporate entity’s headquarters, corporate entity’s country of residence, and corporate entity’s economic activity. Furthermore, it showed that the state of residence and profession of a private entity do not significantly influence money laundering risk. Besides, it exhibited a direct, significant link between behavior suspicion and the customer number, transaction operation code, and the number of counters used by a client. Finally, the outcomes showed that the level of customer behavior suspicion increases while the number of currencies used by a client reduces; nonetheless, this finding was statistically insignificant at the 95% confidence level. Therefore, the research revealed the various aspects that should be considered during the formulation of anti-money laundering policies.